"""Created on Feb 02 03:46:43 2025"""
import numpy as np
from .backend import BaseDistribution, errorHandling as erH
from .utilities_d import (scaled_inv_chi_square_pdf_, scaled_inv_chi_square_log_pdf_, scaled_inv_chi_square_cdf_,
scaled_inv_chi_square_log_cdf_)
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class ScaledInverseChiSquareDistribution(BaseDistribution):
def __init__(self, amplitude: float = 1.0, df: float = 1.0, scale: float = 1.0, loc: float = 0.0,
normalize: bool = False):
if not normalize and amplitude < 0:
raise erH.NegativeAmplitudeError()
if scale < 0:
raise erH.NegativeScaleError()
self.amplitude = 1 if normalize else amplitude
self.df = df
self.scale = scale
self.tau2 = scale / df
self.loc = loc
self.norm = normalize
@classmethod
def scipy_like(cls, a, loc: float = 0.0, scale=1.0):
return cls(df=a, loc=loc, scale=scale, normalize=True)
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def pdf(self, x):
return scaled_inv_chi_square_pdf_(x, amplitude=self.amplitude, df=self.df, scale=self.scale, loc=self.loc,
normalize=self.norm)
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def logpdf(self, x):
return scaled_inv_chi_square_log_pdf_(x, amplitude=self.amplitude, df=self.df, scale=self.scale, loc=self.loc,
normalize=self.norm)
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def cdf(self, x):
return scaled_inv_chi_square_cdf_(x, amplitude=self.amplitude, df=self.df, scale=self.scale, loc=self.loc,
normalize=self.norm)
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def logcdf(self, x):
return scaled_inv_chi_square_log_cdf_(x, amplitude=self.amplitude, df=self.df, loc=self.loc, scale=self.scale,
normalize=self.norm)
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def stats(self):
v, tau2, loc = self.df, self.tau2, self.loc
mean_ = (v * tau2) / (v - 2)
median_ = None
mode_ = (v * tau2) / (v + 2)
variance_ = (2 * v**2 * tau2**2) / ((v - 2)**2 * (v - 4))
return {'mean': mean_ + loc if v > 2 else np.inf,
'median': median_,
'mode': mode_ + loc,
'variance': variance_ if v > 4 else np.inf,
'std': np.sqrt(variance_) if v > 4 else np.inf}